Receptor Modeling Methods

RECEPTOR MODELING METHODS 1263 24.1.3 Empirical Orthogonal Function Receptor Models... 

Ligand recognition in the opioid receptors by modeling methods and design of opioids 98YZ1. 

There are statistical methods to determine the verisimilitude of experimental data to models. One major procedure to do this is nonlinear curve fitting to dose-response curves predicted by receptor models. 

Murine models have been instrumental in representing human disease in vivo. Transgenic approaches have produced mice with genetic defects that promote susceptibility to fibrosis, including abnormal expression of chemokines and/or chemokine receptors. These models have elucidated the factors that are conducive to fibrosis and have thus revealed the central roles of certain chemokines and chemokine receptors. Conventional methods of inducing... 

De Lean, A., Hancock, A. A. and Lefkowitz, R. J. (1982). Validation and statistical analysis of a computer modeling method for quantitative analysis of radioligand binding data for mixtures of pharmacological receptor subtypes, Mol. Pharmacol., 21, 5-16. 

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. 

The two mostly used statistical methods for calculating receptor models are CMB and TTFA. 

A couple of years ago a workshop was organized to compare the performance of the various statistical methods applied for receptor model (12). To create an objective basis for the comparison of the different analyses, a synthetic data set was generated according to the following equation ... 

In this paper the PLS method was introduced as a new tool in calculating statistical receptor models. It was compared with the two most popular methods currently applied to aerosol data Chemical Mass Balance Model and Target Transformation Factor Analysis. The characteristics of the PLS solution were discussed and its advantages over the other methods were pointed out. PLS is especially useful, when both the predictor and response variables are measured with noise and there is high correlation in both blocks. It has been proved in several other chemical applications, that its performance is equal to or better than multiple, stepwise, principal component and ridge regression. Our goal was to create a basis for its environmental chemical application. 

Organic compounds, natural, fossil or anthropogenic, can be used to provide a chemical mass balance for atmospheric particles and a receptor model was developed that relates source contributions to mass concentrations in airborne fine particles. The approach uses organic compound distributions in both source and ambient samples to determine source contributions to the airborne particulate matter. This method was validated for southern California and is being applied in numerous other airsheds. ... 

Aside from applications to specific regions or locations, new developments in receptor modeling have tended to take place in one of three broad categories experimental methods, data analysis and... 

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. 

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... 

This method for obtaining components for receptor models has several advantages relative to other methods. First, the component will be determined at the receptor sites, after any condensation, coagulation or fallout during transit has occurred. Second, It may include fugitive emissions from the source as well as ducted emissions. Third, the measurements are made at the same time and with the same device as the ambient sampling. Fourth, the measurements are made on emissions from specific sources In the local area, not just on the same class of source somewhere else. Fifth, the measurements do not require cooperation of the source operators or Intrusion upon their property. 

In this paper, we have focussed on the weaknesses of our present knowledge about the compositions of particles from sources that are needed as Input for receptor models. However, despite these weaknesses, we feel that the receptor model is probably already capable of more accurate determinations of TSP contributions from various types of sources than the classical methods of source emissions inventories coupled with dispersion models. If the measurements suggested are made, then the receptor models should provide very accurate estimates of those contributions. 

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. 

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