SPATIAL DISTRIBUTED LAG DATA FUSION FOR ESTIMATING AMBIENT AIR POLLUTION.

ABSTRACT: 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 PM_2.5 at unobse

SUBMITTER: Warren JL 

PROVIDER: S-EPMC8189329 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature