ensembleDownscaleR: R Package for Bayesian Ensemble Averaging of PM2.5 Geostatistical Downscalers

Ambient fine particulate matter of size less than 2.5 µm in aerodynamic diameter (PM2.5) is a key component of ambient air pollution that has been linked to numerous adverse health outcomes. Reliable estimates of PM2.5 are important for supporting epidemiologic and health impact assessment studies. Precise measurements of PM2.5 are available through networks of monitors, however these are spatially sparse and temporally incomplete. Chemical transport model (CTM) simulations and satellite-retrieved aerosol optical depth (AOD) measurements are two data sources that have been used to develop prediction models for PM2.5 at fine spatial resolutions with increased spatial coverage. As part of the Multi-Angle Imager for Aerosols (MAIA) project, a geostatistical regression model has been developed to bias-correct AOD, followed by Bayesian ensemble averaging to gap-fill missing AOD values with CTM simulations. Here we present a suite of statistical software (available in the R package ensembleDownscaleR) to facilitate the adaptation of this modeling approach to other settings and air quality modeling applications. We describe the Bayesian ensemble averaging approach, model specifications, estimation methods and evaluation via cross-validation that are implemented in the software. We also provide a case study of estimating PM2.5 using 2018 data from the Los Angeles metropolitan area with an accompanying tutorial. All code is fully reproducible and available at GitHub, data is made available at Zenodo, and the ensembleDownscaleR package is available for download at GitHub.