ensembleDownscaleR: R Package for Bayesian Ensemble Averaging of PM<sub>2.5</sub> Geostatistical Downscalers
Ambient fine particulate matter of size less than 2.5 μm in aerodynamic diameter (PM<sub>2.5</sub>) is a key ambient air pollutant that has been linked to numerous adverse health outcomes. Reliable estimates of PM<sub>2.5</sub> are important for supporting epidemiological and...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/11/1941 |
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| Summary: | Ambient fine particulate matter of size less than 2.5 μm in aerodynamic diameter (PM<sub>2.5</sub>) is a key ambient air pollutant that has been linked to numerous adverse health outcomes. Reliable estimates of PM<sub>2.5</sub> are important for supporting epidemiological and health impact assessment studies. Precise measurements of PM<sub>2.5</sub> 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 PM<sub>2.5</sub> 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 is implemented in the software. We also provide a case study of estimating PM<sub>2.5</sub> using 2018 data from the Los Angeles metropolitan area with an accompanying tutorial. All code is fully reproducible and available on GitHub, data are made on Zenodo, and the ensembleDownscaleR package is available for download on GitHub. |
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| ISSN: | 2072-4292 |