A new approach for combining information available from multiple particulate air pollution monitors.

In time-series studies on the effect of particulate matter (PM) air pollution on an adverse health outcome, PM time-series data are often available from multiple monitoring stations. Published studies have combined the data from the multiple monitors using a simple or trimmed average. We investigate an alternative method of combining the data available from multiple PM-monitoring sites. This method uses time-series data to assign each PM monitor a weight. The weights are then used to combine the data from the multiple PM monitors into a single air pollution time series. The resulting model will identify important monitors for describing the relationship between PM and the adverse health outcome of interest. Subsequent investigations of why certain monitors are more informative than others may provide valuable information concerning the location of vulnerable subpopulations or locations where the meteorological and/or land-use conditions are better for assessing population exposure to PM. The new model is illustrated by applying it to actual data from Cook County, IL, USA and through a simulation study. Using the new model, for the Cook County data, it was found that two of the six monitors provided essentially as much information about the effect of PM on mortality as all six monitors combined. The simulation study suggests that the weights assigned to each monitor by the new model are appropriate, that is, that the model assigns the largest weight to the monitor most highly correlated with the underlying PM time series used to generate mortality.