Spatial modelling of left censored water quality data

Environmental monitoring data is often spatially correlated and left censored. In this paper a previously proposed Bayesian approach to handling spatially correlated data is modified so that bias corrected estimates of variance and spatial correlation parameters are attained. The methodology is applied to a water quality data set from the Ecosystem Health Monitoring Program (EHMP) in south-east Queensland, and the results are contrasted with those from uncorrected for bias variance estimates to show that the latter can lead to unreliable inferences. A simulation study is conducted which shows that the bias corrected estimates of variance and correlation parameters are less biased than uncorrected estimates of these parameters and that the credible intervals for the parameters from bias corrected analyses are wider than those from the uncorrected analyses. The simulation also suggests that predictions of below detection values are generally overestimated by both bias corrected and uncorrected analyses, but the latter predictions are more biased. For predictions of detectable concentrations the simulations suggest that bias corrected and uncorrected analyses are equally biased and both underestimate the true values. Copyright (C) 2009 John Wiley & Sons, Ltd.