PM2.5 mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models
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An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter (PM2.5) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and PM2.5 data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed PM2.5 and AOD data, were used for mapping of PM2.5 over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution (DT3K) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) “Optical_Depth_Land_And_Ocean”. AOD observations were also obtained from the merged DT and DB (deep blue) product (DTB3K) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-PM2.5 with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of PM2.5 from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after PM2.5 mapping. The hotspot and spatial variability of PM2.5 maps can give us a summary of the spatiotemporal patterns of PM2.5 variations.
KeywordsGTWR RANSAC PM2.5 AOD DTB Taiwan
The authors would like to acknowledge NASA Goddard Space Flight Center for MODIS data. We are thankful to Devin White (Oak Ridge National Laboratory) for MODIS Conversion Tool Kit (MCTK).
This research was jointly supported by the National Key Research and Development Program of China (No. 2016YFC1400901), the Startup Foundation for Introduction Talent of NUIST (2017r107), and the MOST and EPA-Taiwan (MOST 105-EPA-F-007-004).
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