Satellite-based high-resolution mapping of ground-level PM2.5 concentrations over East China using a spatiotemporal regression kriging model.

Statistical modeling using ground-based PM2.5 observations and satellite-derived aerosol optical depth (AOD) data is a promising means of obtaining spatially and temporally continuous PM2.5 estimations to assess population exposure to PM2.5. However, the vast amount of AOD data that is missing due to retrieval incapability above bright reflecting surfaces such as cloud/snow cover and urban areas challenge this application. Furthermore, most previous studies cannot directly account for the spatiotemporal autocorrelations in PM2.5 distribution, impacting the associated estimation accuracy. In this study, fixed rank smoothing was adopted to fill the data gaps in a semifinished 3 km AOD dataset, which was a combination of the Moderate Resolution Imaging Spectroradiometer (MODIS) 3 km Dark Target AOD data and MODIS 10 km Deep Blue AOD data from the Terra and Aqua satellites. By matching the gap-filled 3 km AOD data, ground-based PM2.5 observations, and auxiliary variable data, sufficient samples were screened to develop a spatiotemporal regression kriging (STRK) model for PM2.5 estimation. The STRK model achieved notable performance in a cross-validation experiment, with a R square of 0.87 and root-mean-square error of 16.55 μg/m3 when applied to estimate daily ground-level PM2.5 concentrations over East China from March 1, 2015 to February 29, 2016. Using the STRK model, daily PM2.5 concentrations with full spatial coverage at a resolution of 3 km were generated. The PM2.5 distribution pattern over East China can be identified at a relatively fine spatiotemporal scale. Thus, the STRK model with gap-filled high-resolution AOD data can provide reliable full-coverage PM2.5 estimations over large areas for long-term exposure assessment in epidemiological studies.

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