Accounting for Dependence in a Flexible Multivariate Receptor Model

Formulation and evaluation of environmental policy depends on receptor models that are used to assess the number and nature of pollution sources affecting the air quality for a region of interest. Different approaches have been developed for situations in which no information is available about the number and nature of these sources (e.g., exploratory factor analysis) and the composition of these sources is assumed known (e.g., regression and measurement error models). We propose a flexible approach for fitting the receptor model when only partial pollution source information is available. The use of latent variable modeling allows the direct incorporation of subject matter knowledge into the model, including known physical constraints and partial pollution source information obtained from laboratory measurements or past studies. Because air quality data often exhibit temporal and/or spatial dependence, we consider the importance of accounting for such correlation in estimating model parameters and making valid statistical inferences. We propose an approach for incorporating dependence structure directly into estimation and inference procedures via a new nested block bootstrap method that adjusts for bias in estimating moment matrices. A goodness-of-fit test that is valid in the presence of such dependence is proposed. The application of the approach is facilitated by a new multivariate extension of an existing block size determination algorithm. The proposed approaches are evaluated by simulation and illustrated with an analysis of hourly measurements of volatile organic compounds in the El Paso, Texas/Ciudad Juarez, Mexico area.