Department of Geomatics, National Cheng Kung University, Tainan City, Taiwan.
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Environ Sci Pollut Res Int. 2019 Jan;26(2):1902-1910. doi: 10.1007/s11356-018-3763-7. Epub 2018 Nov 20.
An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter (PM) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and PM 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 PM and AOD data, were used for mapping of PM over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution (DT) 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 (DTB) 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-PM 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 PM from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after PM mapping. The hotspot and spatial variability of PM maps can give us a summary of the spatiotemporal patterns of PM variations.
气溶胶光学厚度 (AOD) 与细颗粒物 (PM) 之间的关系存在不确定性，这种不确定性来自气溶胶模型和估算的地表反射率的不确定性、时空分辨率的不匹配、AOD 和 PM 数据的综合以及数据建模。在这项研究中，采用了一种综合的地理时空加权回归 (GTWR) 和随机抽样一致 (RANSAC) 模型，该模型提供了观测 PM 和 AOD 数据之间的良好拟合，用于绘制 2014 年台湾的 PM 图。为此，从科学数据集“Optical_Depth_Land_And_Ocean”中仅获取高质量保证标志 (QA=3) 的 3 公里分辨率的暗目标 (DT) AOD 观测值。还从使用简化合并方案 (SMS) 生成的合并 DT 和 DB（深蓝天）产品 (DTB) 中获取 AOD 观测值，即使用 DT 和 DB 最高质量 AOD 检索值的平均值或可用值的平均值。集成了 RANSAC 的 GTWR 模型可以使用有效采样和拟合来克服观测数据的不确定性和异常值对 AOD-PM 估计的问题。结果表明，该模型能够处理时空异质性和不确定性，是从 RANSAC 子集样本推断 PM 模式的有力工具。此外，在 PM 制图后进行了空间变异性和热点分析。PM 图的热点和空间变异性可以让我们总结 PM 变化的时空模式。
