Source components for aerosol receptor

GORDON ET AL. Source Components for Aerosol Receptor Models 53... 

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

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

Industrial emissions may contribute significantly to the PM10 burden at selected receptor sites as was also shown using the Lenschow approach. To a large part this assessment is due to the assignment of measured secondary aerosol components to industrial emissions of sulphur dioxide and nitrogen oxides which are not produced locally. Statistical receptor models like PMF, on the other hand, are able to identify local industrial impacts which can be seen in elevated levels of trace compounds, but hardly attribute secondary aerosol compounds to any specific industrial source. For example, a PMF study was carried out for a receptor site located a few... 

CMB Application to Central California PM Chow et al. (1992) apportioned source contributions to aerosol concentrations in the San Joaquin Valley of California. The source profiles used for CMB application are shown in Table 26.1. The standard deviations oa.. of the profiles (three or more samples were taken) are also included. To account for secondary aerosol components in the CMB calculations, ammonium sulfate, ammonium nitrate, sodium nitrate, and organic carbon were expressed as secondary source profiles using the stoichiometry of each compound. The average elemental concentrations observed at one of the receptors—Fresno, California, in 1988-1989— are shown in Table 26.2. The ambient concentrations of some species (c.g., Ga, As, Y, Mo, Ag) included in the source profiles were below the detection limits. These species... 

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. 

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. 

The future development of the chemical mass balance receptor model should include 1) more chemical components measured in different size ranges at both source and receptor 2) study of other mathematical methods of solving the chemical mass balance equations 3) validated and documented computer routines for calculations and error estimates and 4) extension of the chemical mass balance to an "aerosol properties balance" to apportion other aerosol indices such as light extinction. 

Results for elements in aerosol samples which are obtained by multielement techniques from data sets from which information about the sources of the components can be extracted (Gordon 1980). Such methods which make use of data obtained at receptor points are called receptor models. The most important receptor models are chemical mass balances (CMB), enrichment factors, time series correlation, multivariate models and spatial models (Cooper and Watson 1980 Gordon 1988). Dispersion modeling has also been used to explain the... 

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