Satellite-based mapping of daily high-resolution ground PM<inf>2.5</inf> in China via space-time regression modeling

Abstract

The use of satellite-retrieved aerosol optical depth (AOD) data and statistical modeling provides a promising approach to estimating PM2.5 concentrations for a large region. However, few studies have conducted high spatial resolution assessments of ground-level PM2.5 for China at the national scale, due to the limitations of high-resolution AOD products. To generate high-resolution PM2.5 for the entirety of mainland China, a combined 3 km AOD dataset was produced by blending the newly released 3 km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target AOD data with the 10 km-resolution MODIS Deep Blue AOD data. Using this dataset, surface PM2.5 measurements, and ancillary information, a space-time regression model that is an improved geographically and temporally weighted regression (GTWR) with an interior point algorithm (IPA)-based efficient mechanism for selecting optimal parameter values, was developed to estimate a large set of daily PM2.5 concentrations. Comparisons with the popular spatiotemporal models (daily geographically weighted regression and two-stage model) indicated that the proposed GTWR model, with an R2 of 0.80 in cross-validation (CV), performs notably better than the two other models, which have an R2 in CV of 0.71 and 0.72, respectively. The use of the combined 3-km high resolution AOD data was found not only to present detailed particle gradients, but also to enhance model performance (CV R2 is only 0.32 for the non-combined AOD data). Furthermore, the GTWR's ability to predict PM2.5 for days without PM2.5-AOD paired samples and to generate historical PM2.5 estimates was demonstrated. As a result, fine-scale PM2.5 maps over China were generated, and several PM2.5 hotspots were identified. Therefore, it becomes possible to generate daily high-resolution PM2.5 estimates over a large area using GTWR, due to its synergy of spatial and temporal dimensions in the data and its ability to extend the temporal coverage of PM2.5 observations.