Hu Hongda, Hu Zhiyong, Zhong Kaiwen, Xu Jianhui, Zhang Feifei, Zhao Yi, Wu Pinghao
Guangzhou Institute of Geography, Guangzhou 510070, China; Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China; Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China.
Department of Earth & Environmental Sciences, University of West Florida, Pensacola 32514, FL, USA.
Sci Total Environ. 2019 Jul 1;672:479-490. doi: 10.1016/j.scitotenv.2019.03.480. Epub 2019 Apr 2.
Statistical modeling using ground-based PM observations and satellite-derived aerosol optical depth (AOD) data is a promising means of obtaining spatially and temporally continuous PM estimations to assess population exposure to PM. 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 PM 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 PM observations, and auxiliary variable data, sufficient samples were screened to develop a spatiotemporal regression kriging (STRK) model for PM 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/m when applied to estimate daily ground-level PM concentrations over East China from March 1, 2015 to February 29, 2016. Using the STRK model, daily PM concentrations with full spatial coverage at a resolution of 3 km were generated. The PM 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 PM estimations over large areas for long-term exposure assessment in epidemiological studies.
利用地面颗粒物（PM）观测数据和卫星反演的气溶胶光学厚度（AOD）数据进行统计建模，是获取时空连续的PM估算值以评估人群PM暴露情况的一种很有前景的方法。然而，由于在云/雪覆盖和城市区域等明亮反射表面上方无法进行反演，大量的AOD数据缺失，这对该应用提出了挑战。此外，大多数先前的研究无法直接考虑PM分布中的时空自相关性，影响了相关的估算精度。在本研究中，采用固定秩平滑法来填补一个半成品3公里AOD数据集的数据空白，该数据集是中分辨率成像光谱仪（MODIS）3公里暗目标AOD数据与来自Terra和Aqua卫星的MODIS 10公里深蓝AOD数据的组合。通过匹配填补空白后的3公里AOD数据、地面PM观测数据和辅助变量数据，筛选出足够的样本，以建立用于PM估算的时空回归克里金（STRK）模型。在交叉验证实验中，STRK模型表现出色，当应用于估算2015年3月1日至2016年2月29日中国东部地区的每日地面PM浓度时，决定系数R平方为0.87，均方根误差为16.55μg/m。利用STRK模型，生成了分辨率为3公里的具有完整空间覆盖的每日PM浓度。可以在相对精细的时空尺度上识别中国东部地区的PM分布模式。因此，结合填补空白后的高分辨率AOD数据的STRK模型，可以为流行病学研究中的长期暴露评估提供大面积可靠的全覆盖PM估算值。
