Identifying Pollution Source Regions Using Multiply Censored Data

Abstract

To improve our understanding of the problem of long-range transport and source−receptor relationships for trace-level toxic air contaminants, we examine the use of several multiple comparison procedures (MCPs) in the analysis and interpretation of multiply-censored data sets. Censoring is a chronic problem for some of the toxic elements of interest (As, Se, Mn, etc.) because their atmospheric concentrations are often too low to be measured precisely. Such concentrations are commonly reported in a nonquantitative way as “below the limit of detection”, leaving the data analyst with censored data sets. Since the standard statistical MCPs are not readily applicable to such data sets, we employ Monte Carlo simulations to evaluate two nonparametric rank-type MCPs for their applicability to the interpretation of censored data. Two different methods for ranking censored data are evaluated: average rank method and substitution with half the detection limit. The results suggest that the Kruskal−Wallis−Dunn MCP with the half-detection limit replacement for censored data is most appropriate for comparing independent, multiply-censored samples of moderate size (20−100 elements). Application of this method to pollutant clusters at several sites in the northeastern USA enabled us to identify potential pollution source regions and atmospheric patterns associated with the long-range transport of air pollutants.