Analyzing weather effects on airborne particulate matter with HGLM.

Particulate matter is one of the six constituent air pollutants regulated by the United States Environmental Protection Agency. In analyzing such data, Bayesian hierarchical models have often been used. In this article we propose the use of hierarchical generalized linear models, which use likelihood inference and have well developed model-checking procedures. Comparisons are made between analyses from hierarchical generalized linear models and Daniels et al.'s (2001) Bayesian models. Model-checking procedure indicates that Daniels et al.'s model can be improved by use of the log-transformation of wind speed and precipitation covariates. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]