Open Access
March 2021 Spatial distributed lag data fusion for estimating ambient air pollution
Joshua L. Warren, Marie Lynn Miranda, Joshua L. Tootoo, Claire E. Osgood, Michelle L. Bell
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Ann. Appl. Stat. 15(1): 323-342 (March 2021). DOI: 10.1214/20-AOAS1399


We introduce spatial (DLfuse) and spatiotemporal (DLfuseST) distributed lag data fusion methods for predicting point-level ambient air pollution concentrations, using, as input, gridded average pollution estimates from a deterministic numerical air quality model. The methods incorporate predictive information from grid cells surrounding the prediction location of interest and are shown to collapse to existing downscaling approaches when this information adds no benefit. The spatial lagged parameters are allowed to vary spatially/spatiotemporally to accommodate the setting where surrounding geographic information is useful in one area/time but not in another. We apply the new methods to predict ambient concentrations of eight-hour maximum ozone and 24-hour average PM2.5 at unobserved spatial locations and times, and compare the predictions with those from several state-of-the-art data fusion approaches. Results show that DLfuse and DLfuseST often provide improved model fit and predictive accuracy when the lagged information is shown to be beneficial. Code to apply the methods is available in the R package DLfuse.


Supported by 1R01MD012769-01A1 from NIMHD and Assistance Agreement No. RD835871 awarded by the U.S. EPA to Yale University. It has not been formally reviewed by EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency.


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Joshua L. Warren. Marie Lynn Miranda. Joshua L. Tootoo. Claire E. Osgood. Michelle L. Bell. "Spatial distributed lag data fusion for estimating ambient air pollution." Ann. Appl. Stat. 15 (1) 323 - 342, March 2021.


Received: 1 November 2019; Revised: 1 September 2020; Published: March 2021
First available in Project Euclid: 18 March 2021

Digital Object Identifier: 10.1214/20-AOAS1399

Keywords: Air pollution , downscaling , spatial distributed lags , varying coefficients

Rights: Copyright © 2021 Institute of Mathematical Statistics

Vol.15 • No. 1 • March 2021
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