Receptor Model Applied to Patterns in Space (RMAPS)

Abstract

The physical and mathematical bases of a new receptor model based on spatially intensive data are presented. The model apportions the average concentration of a species as measured at many sites among several spatially distinct sources and can be applied to primary or secondary species. In the latter case, no assumptions concerning transformation or deposition rates are required. The methodology is a combination of the empirical orthogonal function approach that is well known in meteorology and the self-modeling multivariate modeling approach that has long been applied in chemometrics and multivariate receptor modeling of air quality data. A simple, geometrical example of the modeling approach is given.