Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling
Publication in refereed journal

香港中文大學研究人員
替代計量分析
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其它資訊
摘要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.
出版社接受日期01.03.2018
著者He Qingqing, Huang Bo
期刊名稱Remote Sensing of Environment
出版年份2018
月份3
日期1
卷號206
頁次72 - 83
國際標準期刊號0034-4257
電子國際標準期刊號1879-0704
語言美式英語

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