Articles | Volume 1, issue 1
https://doi.org/10.5194/ascmo-1-59-2015
https://doi.org/10.5194/ascmo-1-59-2015
16 Dec 2015
 | 16 Dec 2015

Autoregressive spatially varying coefficients model for predicting daily PM2.5 using VIIRS satellite AOT

E. M. Schliep, A. E. Gelfand, and D. M. Holland

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Latest update: 19 Jun 2024
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Short summary
There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the US motivates the need for advanced statistical models to predict air quality metrics. We propose a statistical model that jointly models ground-monitoring station data and satellite-obtained data allowing for temporal and spatial misalignment, missingness, and spatially and temporally varying correlation to enhance prediction of particulate matter.