Background: Regulatory monitoring data have been the most common exposure data resource in studies of the association between long-term PM2.5 components and health. However, data collected for regulatory purposes may not be compatible with epidemiological study.
Objectives: We aimed to explore three important features of the PM2.5 component monitoring data obtained from multiple sources to combine all available data for developing spatio-temporal prediction models in the National Particle Component and Toxicity (NPACT) study.
Methods: The NPACT monitoring data were collected in an extensive monitoring campaign targeting cohort participants. The regulatory monitoring data were obtained from the Chemical Speciation Network (CSN) and the Interagency Monitoring of Protected Visual Environments (IMPROVE). We performed exploratory analyses to examine three features that could affect our approach to combining data: comprehensiveness of spatial coverage, comparability of analysis methods, and consistency in sampling protocols. In addition, we considered the viability of developing a spatio-temporal prediction model given: 1) all available data; 2) NPACT data only; and 3) NPACT data with temporal trends estimated from other pollutants.
Results: The number of CSN/IMPROVE monitors was limited in all study areas. The different laboratory analysis methods and the protocol differences for sampling resulted in incompatible measurements between networks. Given these features, we determined that it was preferable to develop our spatio-temporal model using only the NPACT data and under simplifying assumptions.
Conclusions: Investigators conducting epidemiological studies of long-term PM2.5 components need to be mindful of the features of the monitoring data and incorporate this understanding into exposure model development.
Kim, Sun-Young; Sheppard, Lianne; Larson, Timothy V.; Kaufman, Joel; and Vedal, Sverre, "Issues Related to Combining Multiple Speciated PM2.5 Data Sources in Spatio-Temporal Exposure Models for Epidemiology: The NPACT Case Study" (December 2013). UW Biostatistics Working Paper Series. Working Paper 397.